Description :

Microbial communities are primary drivers of element cycles on a planetary level and are essential for the functioning of all ecosystems on earth and are used in many industrial processes. A microbial community is often complex consisting of several populations which can interact with each other by antagonistic or cooperative processes. In our current society, where sustainability and climate compatibility are at the fore, many of such microbial community-dominated processes are considered to be superior to processes based on chemical or physical reactions. As such, the availability of tools and models for behaviour prediction and the proper management of such complex microbial communities, commonly referred to as Microbial Resource Management (MRM), is expected to become increasingly of interest. The understanding of the interactions within such complex microbial communities is essential for developing such MRM tools and models. The last few decades have also seen a tremendous development in disparate disciplines such as molecular and evolutionary ecology (gene transfer, genome evolution, molecular and chemical mediator signalling, etc.) together with quantum leaps in massively efficient technology platforms (genomics, proteomics, metabolomics and the corresponding meta-omics techniques for the molecular analysis of biodiversity). These theoretical and applied advances enable a better understanding, observation, and prediction of microbial processes and communities. This IAP will focus on developing knowledge to generate research hypotheses for MRM, which will enable us to develop novel products and processes, and to improve our environment in the most sustainable way.

The challenge for microbial ecologists and engineers is the optimal management of the microbial resources in order to guarantee stable and functional ecosystems. Therefore the biodiversity-stability relationship and the effect of biodiversity on ecosystem functioning have become major foci in microbial ecology. However biodiversity is a complex term that includes taxonomic, functional, spatial and temporal aspects of organismic diversity. With few exceptions, the majority of studies of biodiversity-functioning and biodiversity-stability theory have been approached in a more phenomenological way, e.g. describing microbial ecosystems by listing species names. Until now, the role of ecological theory in microbial ecology has been neglected and it is our strong belief that advances in microbial ecology are limited by a lack of these conceptual and theoretical approaches. In this project, we will used straightforward approaches using in vitro and in silico “synthetic ecosystems” to test new microbial ecological hypotheses. In the in vitro synthetic ecosystems, different levels of microbial interactions and complexity will be assembled using relevant mixtures of isolated species from culture collections. Since these species are well characterised on a physiological and genomic level, their interactions can be studied in detail using the µ-manager portfolio of molecular techniques. In parallel, in silico synthetic ecosystems will be used to mathematically approach the same ecological questions. This unique coupling between mathematical, experimental ecological theory development and the µ-manager portfolio will permit us to test basic ecological theories on microbial communities, which so far have been blindly adopted from macro-ecology. In this project, the role of (i) invasion (ii) community dynamics, (iii) community structure (richness, evenness), (iv) community architecture, (v) cell to cell interactions, (vii) genetic interactions, (vi) metabolic networking, etc. on ecosystem performance will be examined in detail.

This effort towards a structured research program in MRM has, to our knowledge, not been done before. The findings of this IAP project can have important implications and offer new opportunities for scientists working in domains such as applied and fundamental environmental sciences, food science and even medical microbiology. A combination of new ecological approaches with molecular techniques and mathematical guidance could be used to predict ecosystem function failure and to manage robust biotechnological applications with microbial communities.